Spatiotemporal patterns of mountain whitefish (Prosopium williamsoni) in response to a restoration of longitudinal connectivity

To examine the role of longitudinal connectivity on the spatial and temporal dynamics of mountain whitefish (Prosopium williamsoni), we quantified movement and population dynamics following installation of the Landsburg Dam fishway, Cedar River, WA, USA. Mountain whitefish is widely distributed, poorly studied and not the focus of restoration. Before the fishway, mountain whitefish were not observed above the dam. Here, we focus on snorkel counts collected at reach and mesohabitat (e.g. pools) scales over 11 summers on the 20‐km above‐dam segment following restoration. A camera within the ladder provided number, size and movement timing, thereby informing on behaviour and recolonisation. Segment‐scale abundance increased following fish passage reaching an asymptote in 7 years, and mountain whitefish were detected throughout the main stem in 10 years. Annual movement through the ladder increased over time and was positively correlated with instream abundance and discharge, but negatively correlated with water temperature. About 60% of fish movements occurred in spring and early summer, potentially for foraging opportunities. Reach‐scale abundance peaked between 7 and 10 km from the dam; deep, cool (~10.6 to 11.6°C) conditions characterised these reaches. At the mesohabitat scale, mountain whitefish detection increased with depth and velocity after accounting for distance from the dam. Our results show how restoring longitudinal connectivity allowed this nontarget species to colonise newly available habitat. Their response supports the critical roles of longitudinal connectivity and environmental conditions, that manifest at different spatial scales, in dictating how freshwater fish respond to habitat disturbance.     

Net depth response of a depth-controllable (towed) gillnet for fish sampling

SUMMARY: Flume-tank experiments were performed to examine the depth response of a new type of depth-controlled gillnet. Variations of net depth were investigated as the warp was paid out and wound up for different changes of warp length, main sinker weights, and winch speeds. In most experiments, when the warp finished paying out, the net continued to descend and then ascended slightly to an equilibrium depth (overshoot phenomenon). The overshoot distance was nearly constant when the warp was wound up and increased linearly with increasing winch speed when the warp was paid out. An increase in winch speed reduced net settling time, which converged on a constant value for both paying out and winding up.     

Some Comments on Gerechte Designs

In a companion paper we derived the correct analysis for gerechte designs with uncorrected errors. Here we show that this correct analysis cannot be justified by the usual randomization argument. However, when the regions are rectangular there is a randomization procedure which validates an analysis with three separate error terms. We also outline other developments in design and analysis that may be more satisfactory.     

Evaluation of sampling methods and assessment of the sample size to estimate the weed seedbank in soil, taking into account spatial variability

As field sampling is time consuming, it is necessary to develop efficient sampling techniques to obtain accurate estimates of the weed seedbank in soil. The relative efficiency between sampling schemes depends on the spatial variability in weed seed density across agricultural fields. Spatial variability of the weed seed density was characterized by theoretical correlograms. A systematic sampling (square grill) scheme was considered and it was found that, taking into account spatial variability, this sampling scheme was more efficient than simple random sampling. As a result, the sample size can be reduced in comparison with that given in previous studies, where spatial correlation was ignored. The reduction depends on the correlation structure defined as a function of the ratio, τ, between the nugget effect and the sill of the variogram. The maximum reduction of the sample size, without loss of either precision or confidence level corresponds to the case where there is no nugget effect, τ = 0. The opposite extreme case, where the reduction is nil, corresponds to the case of a pure nugget effect τ = 1. The abaci based on given expressions are provided to determine the sample size in species whose spatial pattern can be fitted either to a Poisson or to a negative binomial distribution.     

土壤养分数据几种特异值处理方法的比较

利用贵州省平坝县马场镇栗木村的13项土壤养分测试数据,对比分析了3σ准则、估计邻域法、影响系数法、邻近点数据比较法4种识别与处理特异值方法的优劣。结果表明:将3σ准则与邻近点数据比较法相结合,对土壤养分测试数据进行特异值处理获得了较好的效果,对提高地质统计学研究结果的精度有积极的作用。     

华西箭竹对岷江冷杉林主要乔木树种幼苗结构及分布格局的影响

在亚高山岷江冷杉林中选取≤50 m2、50～150 m2和＞150 m2的林窗,每种类型内都包含3种华西箭竹盖度(≤20%、20%～50%和＞50%),共调查林窗9个.通过野外群落学调查,研究林窗内岷江冷杉、糙皮桦和川滇长尾槭这3种主要乔木树种幼苗的结构和分布格局.结果表明:1)在华西箭竹盖度大的林窗中,3个树种的各高度级幼苗都不多,林窗形成后,绝大多数林窗被华西箭竹植株定居,竹盖度对主要树种幼苗总密度以及岷江冷杉幼苗密度的影响显著(p＜0.05);2)华西箭竹盖度对岷江冷杉幼苗密度的影响最明显,主要是由于3个树种的生态学特性存在差异,岷江冷杉在没有华西箭竹分布或箭竹盖度小的地方优先更新;3)3种幼苗在9个林窗几乎全为聚集分布,这主要取决于幼苗生物学特性及林窗环境.林窗大小和华西箭竹盖度的综合作用对优势树种幼苗的结构和分布格局产生影响.     

江苏省4类主要秸秆和畜禽粪便空间分布及综合利用潜力研究

为明晰江苏省农业废弃物资源分布格局并进行利用潜力评估,推进农业废弃物资源化利用,助力农业全面绿色转型,本研究借助GIS构建数据批处理模型,测算各区县水稻、小麦、玉米和油菜4种作物的秸秆和猪、牛、羊、家禽4种畜禽的粪便产生量,及其在肥料化、能源化方面的利用潜力理论值,并结合热点分析结果为江苏省农业废弃物综合利用提供政策建议。结果表明：2020年江苏省4种主要作物秸秆可收集总量高达3 972.00万t,以水稻和小麦秸秆为主,集中分布在江苏北部和中部。分析其肥料化利用潜力,理论上相当于1 511.89万t有机碳、12.04万t N、1.59万t P、24.49万t K,能源化理论上相当于1 617.52万t标准煤或6.5×10⁹m³沼气;江苏省4种主要畜禽粪便产生总量为4 560.77万t,家禽粪便与猪粪最多,主要分布在东部边界和西北部地区。分析其肥料化利用潜力,理论计算相当于737.00万t有机碳、31.15万t N、29.86万t P和26.89万t K,能源化利用可折算成1 036.95万t标准煤或4.9×10⁹m     

成都市土地利用时空结构变化研究

随着经济的高速发展,城市化对土地利用结构变化的影响较大,土地利用时空结构变化研究对土地规划及可持续发展具有重要意义.本文对成都市2000—2020年的土地利用数据进行处理,计算土地利用年变化率(R)、土地利用程度综合指数和土地利用变化量(S),得出以下结论:(1)耕地和建设用地变化幅度较大,总体呈增大的趋势;林地和其他...     

Predicting soil depth in a large and complex area using machine learning and environmental correlations

Soil depth is critical for eco-hydrological modeling, carbon storage calculation and land evaluation. However, its spatial variation is poorly understood and rarely mapped. With a limited number of sparse samples, how to predict soil depth in a large area of complex landscapes is still an issue. This study constructed an ensemble machine learning model, i.e.,quantile regression forest, to quantify the relationship between soil depth and environmental conditions. The model was then combined with ...